Say n is the number of examples and m is the number of features, then a naive implementation of a balanced binary decision tree is O(m * n^2 log n). I think scikit-learn's decision tree cache the sorted features, so this reduces to O(m * n log n). Than, to your O(m * n log n) you can multiply the number of decision trees in the forest Best, Sebastian

On Dec 20, 2018, at 1:09 AM, lampahome <pahome.chen@mirlab.org> wrote:

I do some benchmark in my experiments and I almost use ensemble-based regressor.

What is the time complexity if I use random forest regressor? Assume I only set variable estimators=100 and others doesn't enter.

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